Comparison of PSO-based U-Net and SegNet for Automatic Fundus Image Segmentation
Keywords:
Blood vessel, Fundus, Particle-swarm optimization, SegmentationAbstract
The retinal vascularization morphology can be an important cue for various eye pathologies. Past studies have focused on exploring complex image processing and enhancement methods to improve vessel segmentation and detection for screening eye diseases. This research explores the potential of the Particle Swarm Optimization (PSO) method for optimizing segmentation of vessel images without requiring expensive data or computing resources. This optimization framework searches for important hyperparameters for the efficient training of deep-convolutional U-Net and SegNet on a small fundus dataset. The comparison results showed that U-Net achieves better segmentation of fundus photographs with mean overlap measures of 0.74-0.83 than its competing model. A comparison with the state-of-the-art methods showed considerably high classification accuracy and sensitivity scores ranging from 0.93-0.98 were achieved by the proposed networks. This study identified the insufficiency of the employed data augmentation strategies as the main factor responsible for the poor segmentation sensitivity of 0.52-0.63. Future works include optimizing network parameters and adopting effective image preprocessing processes to improve the detection results.
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